Overview

Dataset statistics

Number of variables24
Number of observations3000
Missing cells3958
Missing cells (%)5.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory585.9 KiB
Average record size in memory200.0 B

Variable types

Categorical2
Numeric22

Alerts

customer_spend_02 is highly overall correlated with customer_spend_03 and 5 other fieldsHigh correlation
customer_spend_03 is highly overall correlated with customer_spend_02 and 6 other fieldsHigh correlation
customer_spend_05 is highly overall correlated with customer_spend_02 and 6 other fieldsHigh correlation
customer_spend_06 is highly overall correlated with customer_spend_02 and 6 other fieldsHigh correlation
customer_spend_07 is highly overall correlated with customer_spend_02 and 6 other fieldsHigh correlation
customer_profile_01 is highly overall correlated with customer_spend_02 and 6 other fieldsHigh correlation
customer_profile_02 is highly overall correlated with customer_spend_02 and 6 other fieldsHigh correlation
merchant_profile_02 is highly overall correlated with merchant_profile_03High correlation
merchant_spend_09 is highly overall correlated with merchant_profile_03High correlation
merchant_profile_03 is highly overall correlated with merchant_profile_02 and 1 other fieldsHigh correlation
customer_profile_03 is highly overall correlated with customer_spend_03 and 5 other fieldsHigh correlation
customer_profile_04 is highly overall correlated with customerHigh correlation
customer is highly overall correlated with customer_profile_04High correlation
activation is highly imbalanced (94.5%)Imbalance
customer_spend_01 has 466 (15.5%) missing valuesMissing
customer_spend_02 has 466 (15.5%) missing valuesMissing
customer_spend_03 has 223 (7.4%) missing valuesMissing
customer_spend_05 has 223 (7.4%) missing valuesMissing
customer_spend_06 has 149 (5.0%) missing valuesMissing
customer_spend_07 has 149 (5.0%) missing valuesMissing
merchant_spend_06 has 153 (5.1%) missing valuesMissing
merchant_profile_01 has 153 (5.1%) missing valuesMissing
distance_04 has 531 (17.7%) missing valuesMissing
merchant_profile_02 has 470 (15.7%) missing valuesMissing
merchant_spend_09 has 350 (11.7%) missing valuesMissing
merchant_profile_03 has 350 (11.7%) missing valuesMissing
customer_digital_activity_01 has 154 (5.1%) missing valuesMissing
merchant_spend_10 has 91 (3.0%) missing valuesMissing
customer_profile_01 is highly skewed (γ1 = 20.06843632)Skewed
merchant_spend_10 is highly skewed (γ1 = 20.38585863)Skewed
customer_profile_01 has 361 (12.0%) zerosZeros
customer_profile_02 has 431 (14.4%) zerosZeros
customer_digital_activity_01 has 225 (7.5%) zerosZeros
customer_profile_03 has 185 (6.2%) zerosZeros
customer_digital_activity_02 has 331 (11.0%) zerosZeros

Reproduction

Analysis started2024-03-12 13:16:10.829135
Analysis finished2024-03-12 13:16:29.125686
Duration18.3 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

ind_recommended
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size46.9 KiB
0
2622 
1
378 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2622
87.4%
1 378
 
12.6%

Length

2024-03-12T21:16:29.145914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T21:16:29.176649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2622
87.4%
1 378
 
12.6%

Most occurring characters

ValueCountFrequency (%)
0 2622
87.4%
1 378
 
12.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2622
87.4%
1 378
 
12.6%

Most occurring scripts

ValueCountFrequency (%)
Common 3000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2622
87.4%
1 378
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2622
87.4%
1 378
 
12.6%

activation
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size46.9 KiB
0
2981 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2981
99.4%
1 19
 
0.6%

Length

2024-03-12T21:16:29.203128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T21:16:29.233411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2981
99.4%
1 19
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 2981
99.4%
1 19
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2981
99.4%
1 19
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 3000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2981
99.4%
1 19
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2981
99.4%
1 19
 
0.6%

customer_spend_01
Real number (ℝ)

Distinct2478
Distinct (%)97.8%
Missing466
Missing (%)15.5%
Infinite0
Infinite (%)0.0%
Mean122.76042
Minimum1
Maximum4068.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2024-03-12T21:16:29.266485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13.081625
Q137.972599
median63.289893
Q3114.27981
95-th percentile379.46159
Maximum4068.37
Range4067.37
Interquartile range (IQR)76.307211

Descriptive statistics

Standard deviation241.41387
Coefficient of variation (CV)1.9665448
Kurtosis91.485882
Mean122.76042
Median Absolute Deviation (MAD)31.53712
Skewness8.085047
Sum311074.91
Variance58280.655
MonotonicityNot monotonic
2024-03-12T21:16:29.307120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1 11
 
0.4%
1 10
 
0.3%
100 6
 
0.2%
9.99 5
 
0.2%
25 5
 
0.2%
44.91 3
 
0.1%
4 3
 
0.1%
13.99 3
 
0.1%
46.7525 2
 
0.1%
2.99 2
 
0.1%
Other values (2468) 2484
82.8%
(Missing) 466
 
15.5%
ValueCountFrequency (%)
1 10
0.3%
1.05 1
 
< 0.1%
1.075 1
 
< 0.1%
1.1 11
0.4%
1.6 1
 
< 0.1%
1.605 1
 
< 0.1%
1.99 2
 
0.1%
2 1
 
< 0.1%
2.08 1
 
< 0.1%
2.11 1
 
< 0.1%
ValueCountFrequency (%)
4068.37 1
< 0.1%
3500 1
< 0.1%
3400.281667 1
< 0.1%
2965.12 1
< 0.1%
2933.5 1
< 0.1%
2921.2 1
< 0.1%
2665.14625 1
< 0.1%
2332.878 1
< 0.1%
1927.5225 1
< 0.1%
1744.074 1
< 0.1%

customer_spend_02
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)1.6%
Missing466
Missing (%)15.5%
Infinite0
Infinite (%)0.0%
Mean7.5114444
Minimum1
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2024-03-12T21:16:29.347550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q311
95-th percentile21
Maximum47
Range46
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.4515311
Coefficient of variation (CV)0.85889355
Kurtosis3.2375496
Mean7.5114444
Median Absolute Deviation (MAD)4
Skewness1.5259305
Sum19034
Variance41.622253
MonotonicityNot monotonic
2024-03-12T21:16:29.382731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1 356
11.9%
2 280
9.3%
3 225
 
7.5%
4 198
 
6.6%
5 190
 
6.3%
6 153
 
5.1%
8 134
 
4.5%
7 128
 
4.3%
10 107
 
3.6%
9 101
 
3.4%
Other values (30) 662
22.1%
(Missing) 466
15.5%
ValueCountFrequency (%)
1 356
11.9%
2 280
9.3%
3 225
7.5%
4 198
6.6%
5 190
6.3%
6 153
5.1%
7 128
 
4.3%
8 134
 
4.5%
9 101
 
3.4%
10 107
 
3.6%
ValueCountFrequency (%)
47 2
0.1%
43 1
< 0.1%
42 1
< 0.1%
41 2
0.1%
38 1
< 0.1%
37 1
< 0.1%
36 1
< 0.1%
35 2
0.1%
34 1
< 0.1%
33 1
< 0.1%

customer_spend_03
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct214
Distinct (%)7.7%
Missing223
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean45.594166
Minimum0
Maximum340
Zeros5
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2024-03-12T21:16:29.739503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median28
Q366
95-th percentile147
Maximum340
Range340
Interquartile range (IQR)58

Descriptive statistics

Standard deviation49.047812
Coefficient of variation (CV)1.0757475
Kurtosis2.8063865
Mean45.594166
Median Absolute Deviation (MAD)23
Skewness1.5997882
Sum126615
Variance2405.6879
MonotonicityNot monotonic
2024-03-12T21:16:29.777186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 136
 
4.5%
2 101
 
3.4%
3 92
 
3.1%
4 87
 
2.9%
5 82
 
2.7%
6 66
 
2.2%
7 66
 
2.2%
8 64
 
2.1%
9 55
 
1.8%
11 50
 
1.7%
Other values (204) 1978
65.9%
(Missing) 223
 
7.4%
ValueCountFrequency (%)
0 5
 
0.2%
1 136
4.5%
2 101
3.4%
3 92
3.1%
4 87
2.9%
5 82
2.7%
6 66
2.2%
7 66
2.2%
8 64
2.1%
9 55
1.8%
ValueCountFrequency (%)
340 1
< 0.1%
329 1
< 0.1%
301 1
< 0.1%
293 1
< 0.1%
264 2
0.1%
260 1
< 0.1%
256 1
< 0.1%
245 1
< 0.1%
241 1
< 0.1%
237 1
< 0.1%

customer_spend_05
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2762
Distinct (%)99.5%
Missing223
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean13926.256
Minimum1.05
Maximum675640.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2024-03-12T21:16:29.818826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.05
5-th percentile160.66
Q11617.78
median6064
Q314724.93
95-th percentile47487.914
Maximum675640.04
Range675638.99
Interquartile range (IQR)13107.15

Descriptive statistics

Standard deviation32900.36
Coefficient of variation (CV)2.3624699
Kurtosis145.70033
Mean13926.256
Median Absolute Deviation (MAD)5210.68
Skewness10.10774
Sum38673213
Variance1.0824337 × 109
MonotonicityNot monotonic
2024-03-12T21:16:29.857380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1 5
 
0.2%
83.94 2
 
0.1%
23616.32 2
 
0.1%
2485.77 2
 
0.1%
3.3 2
 
0.1%
1741.9 2
 
0.1%
164.84 2
 
0.1%
1079.44 2
 
0.1%
810.39 2
 
0.1%
50 2
 
0.1%
Other values (2752) 2754
91.8%
(Missing) 223
 
7.4%
ValueCountFrequency (%)
1.05 1
 
< 0.1%
1.1 5
0.2%
2 1
 
< 0.1%
2.1 1
 
< 0.1%
3.3 2
 
0.1%
5 1
 
< 0.1%
5.98 1
 
< 0.1%
6 1
 
< 0.1%
9.69 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
675640.04 1
< 0.1%
537614.72 1
< 0.1%
505937.2 1
< 0.1%
505714.37 1
< 0.1%
478051.89 1
< 0.1%
357934.71 1
< 0.1%
347973.74 1
< 0.1%
260318.31 1
< 0.1%
242669.49 1
< 0.1%
232488.2 1
< 0.1%

customer_spend_06
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct768
Distinct (%)26.9%
Missing149
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean233.03613
Minimum1
Maximum2234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2024-03-12T21:16:29.899302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q138
median127
Q3329
95-th percentile798
Maximum2234
Range2233
Interquartile range (IQR)291

Descriptive statistics

Standard deviation276.4519
Coefficient of variation (CV)1.1863049
Kurtosis5.4344277
Mean233.03613
Median Absolute Deviation (MAD)109
Skewness2.0297793
Sum664386
Variance76425.65
MonotonicityNot monotonic
2024-03-12T21:16:29.935150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 44
 
1.5%
2 34
 
1.1%
4 33
 
1.1%
3 29
 
1.0%
8 28
 
0.9%
6 26
 
0.9%
13 25
 
0.8%
12 24
 
0.8%
5 24
 
0.8%
16 23
 
0.8%
Other values (758) 2561
85.4%
(Missing) 149
 
5.0%
ValueCountFrequency (%)
1 44
1.5%
2 34
1.1%
3 29
1.0%
4 33
1.1%
5 24
0.8%
6 26
0.9%
7 18
0.6%
8 28
0.9%
9 21
0.7%
10 18
0.6%
ValueCountFrequency (%)
2234 1
< 0.1%
1866 1
< 0.1%
1864 1
< 0.1%
1861 1
< 0.1%
1850 1
< 0.1%
1729 1
< 0.1%
1636 1
< 0.1%
1632 1
< 0.1%
1615 1
< 0.1%
1517 1
< 0.1%

customer_spend_07
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct348
Distinct (%)12.2%
Missing149
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean113.73202
Minimum1
Maximum359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2024-03-12T21:16:29.972870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q128
median86
Q3186
95-th percentile300
Maximum359
Range358
Interquartile range (IQR)158

Descriptive statistics

Standard deviation97.215823
Coefficient of variation (CV)0.85477968
Kurtosis-0.72902018
Mean113.73202
Median Absolute Deviation (MAD)69
Skewness0.67934559
Sum324250
Variance9450.9162
MonotonicityNot monotonic
2024-03-12T21:16:30.010879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 56
 
1.9%
2 39
 
1.3%
4 37
 
1.2%
5 35
 
1.2%
7 34
 
1.1%
3 33
 
1.1%
13 33
 
1.1%
14 32
 
1.1%
12 30
 
1.0%
6 30
 
1.0%
Other values (338) 2492
83.1%
(Missing) 149
 
5.0%
ValueCountFrequency (%)
1 56
1.9%
2 39
1.3%
3 33
1.1%
4 37
1.2%
5 35
1.2%
6 30
1.0%
7 34
1.1%
8 29
1.0%
9 23
0.8%
10 13
 
0.4%
ValueCountFrequency (%)
359 1
 
< 0.1%
356 1
 
< 0.1%
355 2
0.1%
354 1
 
< 0.1%
349 4
0.1%
348 3
0.1%
346 3
0.1%
345 3
0.1%
344 2
0.1%
342 3
0.1%

merchant_spend_06
Real number (ℝ)

Distinct101
Distinct (%)3.5%
Missing153
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean-80.824025
Minimum-999
Maximum100
Zeros0
Zeros (%)0.0%
Negative328
Negative (%)10.9%
Memory size46.9 KiB
2024-03-12T21:16:30.051932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q111
median29
Q355
95-th percentile86
Maximum100
Range1099
Interquartile range (IQR)44

Descriptive statistics

Standard deviation332.29112
Coefficient of variation (CV)-4.1112914
Kurtosis3.7489553
Mean-80.824025
Median Absolute Deviation (MAD)21
Skewness-2.3857446
Sum-230106
Variance110417.39
MonotonicityNot monotonic
2024-03-12T21:16:30.088355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 328
 
10.9%
17 52
 
1.7%
12 52
 
1.7%
2 49
 
1.6%
11 48
 
1.6%
8 47
 
1.6%
15 46
 
1.5%
19 45
 
1.5%
10 44
 
1.5%
21 44
 
1.5%
Other values (91) 2092
69.7%
(Missing) 153
 
5.1%
ValueCountFrequency (%)
-999 328
10.9%
1 18
 
0.6%
2 49
 
1.6%
3 34
 
1.1%
4 41
 
1.4%
5 34
 
1.1%
6 30
 
1.0%
7 43
 
1.4%
8 47
 
1.6%
9 37
 
1.2%
ValueCountFrequency (%)
100 11
0.4%
99 5
0.2%
98 11
0.4%
97 11
0.4%
96 10
0.3%
95 12
0.4%
94 9
0.3%
93 7
0.2%
92 7
0.2%
91 11
0.4%

merchant_profile_01
Real number (ℝ)

Distinct50
Distinct (%)1.8%
Missing153
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean264.61574
Minimum101
Maximum502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2024-03-12T21:16:30.127631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1101
median324
Q3406
95-th percentile419
Maximum502
Range401
Interquartile range (IQR)305

Descriptive statistics

Standard deviation137.08052
Coefficient of variation (CV)0.51803615
Kurtosis-1.7422119
Mean264.61574
Median Absolute Deviation (MAD)93
Skewness-0.15948455
Sum753361
Variance18791.068
MonotonicityNot monotonic
2024-03-12T21:16:30.166479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 774
25.8%
406 369
12.3%
326 299
 
10.0%
103 273
 
9.1%
202 182
 
6.1%
416 100
 
3.3%
417 100
 
3.3%
419 82
 
2.7%
403 71
 
2.4%
307 64
 
2.1%
Other values (40) 533
17.8%
(Missing) 153
 
5.1%
ValueCountFrequency (%)
101 774
25.8%
102 13
 
0.4%
103 273
 
9.1%
201 13
 
0.4%
202 182
 
6.1%
301 3
 
0.1%
302 1
 
< 0.1%
303 3
 
0.1%
306 1
 
< 0.1%
307 64
 
2.1%
ValueCountFrequency (%)
502 2
 
0.1%
501 6
 
0.2%
420 61
2.0%
419 82
2.7%
418 4
 
0.1%
417 100
3.3%
416 100
3.3%
415 47
1.6%
414 38
 
1.3%
413 63
2.1%

customer_profile_01
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2589
Distinct (%)86.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2383.037
Minimum-999
Maximum282096.02
Zeros361
Zeros (%)12.0%
Negative5
Negative (%)0.2%
Memory size46.9 KiB
2024-03-12T21:16:30.208962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile0
Q190.325
median622.54
Q32269.32
95-th percentile9226.368
Maximum282096.02
Range283095.02
Interquartile range (IQR)2178.995

Descriptive statistics

Standard deviation8243.3007
Coefficient of variation (CV)3.4591577
Kurtosis578.95469
Mean2383.037
Median Absolute Deviation (MAD)617.11
Skewness20.068436
Sum7146727.9
Variance67952006
MonotonicityNot monotonic
2024-03-12T21:16:30.245485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 361
 
12.0%
-999 5
 
0.2%
0.99 4
 
0.1%
15 3
 
0.1%
99 3
 
0.1%
20 3
 
0.1%
70 3
 
0.1%
95 3
 
0.1%
2.99 3
 
0.1%
337.69 2
 
0.1%
Other values (2579) 2609
87.0%
ValueCountFrequency (%)
-999 5
 
0.2%
0 361
12.0%
0.07 1
 
< 0.1%
0.83 1
 
< 0.1%
0.99 4
 
0.1%
1 1
 
< 0.1%
1.35 1
 
< 0.1%
1.47 1
 
< 0.1%
1.69 1
 
< 0.1%
1.77 1
 
< 0.1%
ValueCountFrequency (%)
282096.02 1
< 0.1%
204934.9 1
< 0.1%
96577.08 1
< 0.1%
96420.66 1
< 0.1%
67924.24 1
< 0.1%
61138.92 1
< 0.1%
58862.49 1
< 0.1%
51610.85 1
< 0.1%
51161.28 1
< 0.1%
48206.6 1
< 0.1%

customer_profile_02
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2162
Distinct (%)72.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2013.9796
Minimum0
Maximum217279.88
Zeros431
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2024-03-12T21:16:30.285659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q195.49
median511.83
Q31952.765
95-th percentile7792.019
Maximum217279.88
Range217279.88
Interquartile range (IQR)1857.275

Descriptive statistics

Standard deviation6292.6802
Coefficient of variation (CV)3.1245004
Kurtosis528.48682
Mean2013.9796
Median Absolute Deviation (MAD)511.83
Skewness18.429191
Sum6039924.9
Variance39597823
MonotonicityNot monotonic
2024-03-12T21:16:30.322738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 431
 
14.4%
200 38
 
1.3%
100 37
 
1.2%
40 35
 
1.2%
300 28
 
0.9%
1000 20
 
0.7%
400 20
 
0.7%
50 20
 
0.7%
500 19
 
0.6%
2000 18
 
0.6%
Other values (2152) 2333
77.8%
ValueCountFrequency (%)
0 431
14.4%
0.99 3
 
0.1%
1 1
 
< 0.1%
1.3 1
 
< 0.1%
1.36 1
 
< 0.1%
1.47 1
 
< 0.1%
1.77 1
 
< 0.1%
1.99 1
 
< 0.1%
2.23 1
 
< 0.1%
2.78 1
 
< 0.1%
ValueCountFrequency (%)
217279.88 1
< 0.1%
122098.63 1
< 0.1%
102156.57 1
< 0.1%
65246.43 1
< 0.1%
42056.6 1
< 0.1%
40250.21 1
< 0.1%
38553.36 1
< 0.1%
35764.91 1
< 0.1%
34789.14 1
< 0.1%
34763.13 1
< 0.1%

distance_04
Real number (ℝ)

Distinct1071
Distinct (%)43.4%
Missing531
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean26.048651
Minimum1
Maximum2923
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2024-03-12T21:16:30.361943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.3684211
median3.4285714
Q36.9280822
95-th percentile42.3
Maximum2923
Range2922
Interquartile range (IQR)5.5596611

Descriptive statistics

Standard deviation152.39291
Coefficient of variation (CV)5.8503188
Kurtosis146.63413
Mean26.048651
Median Absolute Deviation (MAD)2.4285714
Skewness10.96521
Sum64314.118
Variance23223.599
MonotonicityNot monotonic
2024-03-12T21:16:30.397849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 556
 
18.5%
3 68
 
2.3%
2 55
 
1.8%
4 49
 
1.6%
5 46
 
1.5%
7 33
 
1.1%
6 32
 
1.1%
1.5 30
 
1.0%
2.5 22
 
0.7%
3.5 22
 
0.7%
Other values (1061) 1556
51.9%
(Missing) 531
 
17.7%
ValueCountFrequency (%)
1 556
18.5%
1.00877193 1
 
< 0.1%
1.012195122 1
 
< 0.1%
1.016129032 1
 
< 0.1%
1.02173913 1
 
< 0.1%
1.043478261 1
 
< 0.1%
1.0625 2
 
0.1%
1.076470588 1
 
< 0.1%
1.083333333 2
 
0.1%
1.099547511 1
 
< 0.1%
ValueCountFrequency (%)
2923 1
< 0.1%
2410 1
< 0.1%
2234 1
< 0.1%
2191.5 1
< 0.1%
2124.5 1
< 0.1%
1566 1
< 0.1%
1442 1
< 0.1%
1363 1
< 0.1%
1311 1
< 0.1%
1218.75 2
0.1%

merchant_profile_02
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1339
Distinct (%)52.9%
Missing470
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean0.25136885
Minimum0.011764706
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2024-03-12T21:16:30.437943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.011764706
5-th percentile0.070890807
Q10.14699525
median0.2205591
Q30.32220358
95-th percentile0.5103322
Maximum1
Range0.98823529
Interquartile range (IQR)0.17520833

Descriptive statistics

Standard deviation0.15405411
Coefficient of variation (CV)0.61286076
Kurtosis5.1661336
Mean0.25136885
Median Absolute Deviation (MAD)0.082820197
Skewness1.7747746
Sum635.9632
Variance0.023732668
MonotonicityNot monotonic
2024-03-12T21:16:30.476415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3333333333 71
 
2.4%
0.25 68
 
2.3%
0.5 65
 
2.2%
0.2 55
 
1.8%
0.1666666667 46
 
1.5%
0.1428571429 39
 
1.3%
0.125 30
 
1.0%
0.2222222222 27
 
0.9%
0.1111111111 26
 
0.9%
1 26
 
0.9%
Other values (1329) 2077
69.2%
(Missing) 470
 
15.7%
ValueCountFrequency (%)
0.01176470588 1
< 0.1%
0.01204819277 1
< 0.1%
0.01639344262 1
< 0.1%
0.01886792453 1
< 0.1%
0.02150537634 1
< 0.1%
0.02325581395 1
< 0.1%
0.024 1
< 0.1%
0.025 2
0.1%
0.02816901408 1
< 0.1%
0.02941176471 2
0.1%
ValueCountFrequency (%)
1 26
0.9%
0.8571428571 1
 
< 0.1%
0.8235294118 1
 
< 0.1%
0.8181818182 1
 
< 0.1%
0.8 2
 
0.1%
0.75 10
 
0.3%
0.7272727273 1
 
< 0.1%
0.7142857143 1
 
< 0.1%
0.7 2
 
0.1%
0.6923076923 1
 
< 0.1%

merchant_spend_09
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2468
Distinct (%)93.1%
Missing350
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean22033.547
Minimum5
Maximum103342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2024-03-12T21:16:30.515281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile772.35
Q13974.75
median11901.5
Q328103.25
95-th percentile83254
Maximum103342
Range103337
Interquartile range (IQR)24128.5

Descriptive statistics

Standard deviation25397.38
Coefficient of variation (CV)1.1526687
Kurtosis1.6052973
Mean22033.547
Median Absolute Deviation (MAD)9441.5
Skewness1.5753381
Sum58388900
Variance6.4502692 × 108
MonotonicityNot monotonic
2024-03-12T21:16:30.555184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13166 4
 
0.1%
28919 4
 
0.1%
18755 4
 
0.1%
4529 3
 
0.1%
30738 3
 
0.1%
1967 3
 
0.1%
5 3
 
0.1%
3732 3
 
0.1%
934 3
 
0.1%
98664 3
 
0.1%
Other values (2458) 2617
87.2%
(Missing) 350
 
11.7%
ValueCountFrequency (%)
5 3
0.1%
22 1
 
< 0.1%
32 1
 
< 0.1%
50 1
 
< 0.1%
52 1
 
< 0.1%
62 1
 
< 0.1%
79 1
 
< 0.1%
80 1
 
< 0.1%
87 1
 
< 0.1%
89 1
 
< 0.1%
ValueCountFrequency (%)
103342 1
< 0.1%
103205 1
< 0.1%
103154 1
< 0.1%
102974 1
< 0.1%
102869 1
< 0.1%
102658 1
< 0.1%
102620 1
< 0.1%
102524 1
< 0.1%
102190 1
< 0.1%
102187 1
< 0.1%

merchant_profile_03
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1931
Distinct (%)72.9%
Missing350
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean21546.895
Minimum1
Maximum98638
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2024-03-12T21:16:30.594667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile603
Q13909.25
median11837
Q327733
95-th percentile82005.85
Maximum98638
Range98637
Interquartile range (IQR)23823.75

Descriptive statistics

Standard deviation24817.976
Coefficient of variation (CV)1.1518121
Kurtosis1.6271689
Mean21546.895
Median Absolute Deviation (MAD)9502.5
Skewness1.5796443
Sum57099273
Variance6.1593191 × 108
MonotonicityNot monotonic
2024-03-12T21:16:30.634013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24708 47
 
1.6%
36939 44
 
1.5%
98638 19
 
0.6%
16599 17
 
0.6%
7236 14
 
0.5%
11795 11
 
0.4%
23900 10
 
0.3%
5578 10
 
0.3%
16202 10
 
0.3%
1593 9
 
0.3%
Other values (1921) 2459
82.0%
(Missing) 350
 
11.7%
ValueCountFrequency (%)
1 8
0.3%
24 1
 
< 0.1%
27 1
 
< 0.1%
28 1
 
< 0.1%
37 1
 
< 0.1%
46 1
 
< 0.1%
47 1
 
< 0.1%
55 1
 
< 0.1%
62 2
 
0.1%
69 1
 
< 0.1%
ValueCountFrequency (%)
98638 19
0.6%
98623 1
 
< 0.1%
98553 1
 
< 0.1%
98499 1
 
< 0.1%
98445 1
 
< 0.1%
98189 1
 
< 0.1%
98177 1
 
< 0.1%
97954 1
 
< 0.1%
97882 1
 
< 0.1%
97708 1
 
< 0.1%

customer_digital_activity_01
Real number (ℝ)

MISSING  ZEROS 

Distinct755
Distinct (%)26.5%
Missing154
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean0.54052719
Minimum0
Maximum1
Zeros225
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2024-03-12T21:16:30.672662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.22222222
median0.55813953
Q30.8862013
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.66397908

Descriptive statistics

Standard deviation0.34613197
Coefficient of variation (CV)0.64035996
Kurtosis-1.3879777
Mean0.54052719
Median Absolute Deviation (MAD)0.33074935
Skewness-0.10744967
Sum1538.3404
Variance0.11980734
MonotonicityNot monotonic
2024-03-12T21:16:30.712106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 399
 
13.3%
0 225
 
7.5%
0.5 47
 
1.6%
0.3333333333 28
 
0.9%
0.6666666667 25
 
0.8%
0.8 24
 
0.8%
0.2 24
 
0.8%
0.25 19
 
0.6%
0.6 17
 
0.6%
0.1428571429 16
 
0.5%
Other values (745) 2022
67.4%
(Missing) 154
 
5.1%
ValueCountFrequency (%)
0 225
7.5%
0.01818181818 1
 
< 0.1%
0.02 1
 
< 0.1%
0.02040816327 1
 
< 0.1%
0.02083333333 1
 
< 0.1%
0.02127659574 1
 
< 0.1%
0.02173913043 1
 
< 0.1%
0.02272727273 1
 
< 0.1%
0.02325581395 3
 
0.1%
0.02380952381 1
 
< 0.1%
ValueCountFrequency (%)
1 399
13.3%
0.9871794872 1
 
< 0.1%
0.9852941176 1
 
< 0.1%
0.9838709677 1
 
< 0.1%
0.9818181818 1
 
< 0.1%
0.9814814815 1
 
< 0.1%
0.9803921569 3
 
0.1%
0.98 2
 
0.1%
0.9795918367 3
 
0.1%
0.9791666667 1
 
< 0.1%

merchant_spend_10
Real number (ℝ)

MISSING  SKEWED 

Distinct2562
Distinct (%)88.1%
Missing91
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean241.02612
Minimum1
Maximum39351.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2024-03-12T21:16:30.752256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.581
Q123.265
median43
Q3116.14286
95-th percentile731.416
Maximum39351.15
Range39350.15
Interquartile range (IQR)92.877857

Descriptive statistics

Standard deviation1614.1903
Coefficient of variation (CV)6.6971591
Kurtosis458.38468
Mean241.02612
Median Absolute Deviation (MAD)26.5
Skewness20.385859
Sum701144.99
Variance2605610.2
MonotonicityNot monotonic
2024-03-12T21:16:30.791363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 10
 
0.3%
25 8
 
0.3%
30 8
 
0.3%
45 8
 
0.3%
10 7
 
0.2%
50 7
 
0.2%
35 7
 
0.2%
150 6
 
0.2%
450 6
 
0.2%
20 6
 
0.2%
Other values (2552) 2836
94.5%
(Missing) 91
 
3.0%
ValueCountFrequency (%)
1 2
0.1%
1.1 1
< 0.1%
1.344364407 1
< 0.1%
1.491869301 1
< 0.1%
1.500229358 1
< 0.1%
1.7 1
< 0.1%
2.233333333 1
< 0.1%
2.99 1
< 0.1%
3.088461538 1
< 0.1%
3.34 1
< 0.1%
ValueCountFrequency (%)
39351.15 3
0.1%
33405 1
 
< 0.1%
30103.5 1
 
< 0.1%
14182.85 1
 
< 0.1%
9675.65 1
 
< 0.1%
9603.1 1
 
< 0.1%
8574.75 1
 
< 0.1%
8261.28 1
 
< 0.1%
5040.5 1
 
< 0.1%
4823.6 1
 
< 0.1%

customer_profile_03
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2564
Distinct (%)86.0%
Missing18
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean48.241643
Minimum0
Maximum100
Zeros185
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2024-03-12T21:16:30.833726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.106729
median46.192729
Q382.796235
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)69.689506

Descriptive statistics

Standard deviation35.511071
Coefficient of variation (CV)0.73610824
Kurtosis-1.4634886
Mean48.241643
Median Absolute Deviation (MAD)34.416606
Skewness0.090105935
Sum143856.58
Variance1261.0361
MonotonicityNot monotonic
2024-03-12T21:16:30.873180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 227
 
7.6%
0 185
 
6.2%
7.614311559 2
 
0.1%
13.14173912 2
 
0.1%
65.19974134 2
 
0.1%
4.057659421 2
 
0.1%
5.600361333 2
 
0.1%
32.30505643 2
 
0.1%
85.75350667 2
 
0.1%
35.22849304 2
 
0.1%
Other values (2554) 2554
85.1%
(Missing) 18
 
0.6%
ValueCountFrequency (%)
0 185
6.2%
0.003083673375 1
 
< 0.1%
0.004582147064 1
 
< 0.1%
0.004976753018 1
 
< 0.1%
0.006537953996 1
 
< 0.1%
0.008825346195 1
 
< 0.1%
0.009515782887 1
 
< 0.1%
0.01917867199 1
 
< 0.1%
0.02057299449 1
 
< 0.1%
0.03872742123 1
 
< 0.1%
ValueCountFrequency (%)
100 227
7.6%
99.99257197 1
 
< 0.1%
99.97503836 1
 
< 0.1%
99.96526308 1
 
< 0.1%
99.94986176 1
 
< 0.1%
99.94672 1
 
< 0.1%
99.92505689 1
 
< 0.1%
99.92277528 1
 
< 0.1%
99.91938133 1
 
< 0.1%
99.91474588 1
 
< 0.1%
Distinct284
Distinct (%)9.5%
Missing7
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean6.6884397
Minimum0
Maximum197.83333
Zeros331
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2024-03-12T21:16:30.912240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.83333333
median2.1666667
Q35.3333333
95-th percentile33.5
Maximum197.83333
Range197.83333
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation14.401595
Coefficient of variation (CV)2.1532069
Kurtosis39.566888
Mean6.6884397
Median Absolute Deviation (MAD)1.8333333
Skewness5.2103906
Sum20018.5
Variance207.40593
MonotonicityNot monotonic
2024-03-12T21:16:30.950322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 331
 
11.0%
0.1666666667 131
 
4.4%
1 122
 
4.1%
1.166666667 111
 
3.7%
0.8333333333 108
 
3.6%
1.333333333 107
 
3.6%
1.666666667 100
 
3.3%
1.5 98
 
3.3%
0.3333333333 81
 
2.7%
0.6666666667 76
 
2.5%
Other values (274) 1728
57.6%
ValueCountFrequency (%)
0 331
11.0%
0.1666666667 131
 
4.4%
0.3333333333 81
 
2.7%
0.5 74
 
2.5%
0.6666666667 76
 
2.5%
0.8333333333 108
 
3.6%
1 122
 
4.1%
1.166666667 111
 
3.7%
1.333333333 107
 
3.6%
1.5 98
 
3.3%
ValueCountFrequency (%)
197.8333333 1
 
< 0.1%
174.8333333 1
 
< 0.1%
162.5 1
 
< 0.1%
154.6666667 1
 
< 0.1%
137.1666667 1
 
< 0.1%
127.8333333 1
 
< 0.1%
117.3333333 1
 
< 0.1%
113.6666667 3
0.1%
109 1
 
< 0.1%
108.6666667 1
 
< 0.1%

customer_profile_04
Real number (ℝ)

Distinct533
Distinct (%)17.8%
Missing3
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean176.44511
Minimum2
Maximum744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2024-03-12T21:16:30.990169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile18
Q164
median149
Q3251
95-th percentile461.2
Maximum744
Range742
Interquartile range (IQR)187

Descriptive statistics

Standard deviation140.2621
Coefficient of variation (CV)0.79493334
Kurtosis0.66385496
Mean176.44511
Median Absolute Deviation (MAD)92
Skewness1.0474277
Sum528806
Variance19673.457
MonotonicityNot monotonic
2024-03-12T21:16:31.026630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 41
 
1.4%
52 24
 
0.8%
79 23
 
0.8%
67 22
 
0.7%
41 21
 
0.7%
64 19
 
0.6%
21 19
 
0.6%
175 19
 
0.6%
257 18
 
0.6%
75 18
 
0.6%
Other values (523) 2773
92.4%
ValueCountFrequency (%)
2 5
 
0.2%
3 16
0.5%
4 12
0.4%
5 9
0.3%
6 13
0.4%
7 10
0.3%
8 7
0.2%
9 13
0.4%
10 11
0.4%
11 5
 
0.2%
ValueCountFrequency (%)
744 1
< 0.1%
685 1
< 0.1%
678 1
< 0.1%
676 1
< 0.1%
674 1
< 0.1%
666 1
< 0.1%
659 1
< 0.1%
656 1
< 0.1%
653 1
< 0.1%
648 1
< 0.1%

distance_05
Real number (ℝ)

Distinct2988
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1853933
Minimum0.040288661
Maximum255.97998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2024-03-12T21:16:31.065933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.040288661
5-th percentile0.45828968
Q11.8904806
median4.0057958
Q37.953539
95-th percentile19.727978
Maximum255.97998
Range255.93969
Interquartile range (IQR)6.0630584

Descriptive statistics

Standard deviation7.8062318
Coefficient of variation (CV)1.2620429
Kurtosis356.02905
Mean6.1853933
Median Absolute Deviation (MAD)2.5669797
Skewness12.334938
Sum18556.18
Variance60.937255
MonotonicityNot monotonic
2024-03-12T21:16:31.105202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1885755869 4
 
0.1%
0.1238412933 4
 
0.1%
5.006358535 2
 
0.1%
0.1236240723 2
 
0.1%
0.4204664203 2
 
0.1%
0.6469576716 2
 
0.1%
0.2085569932 2
 
0.1%
6.964461683 2
 
0.1%
0.04028866073 1
 
< 0.1%
6.164381144 1
 
< 0.1%
Other values (2978) 2978
99.3%
ValueCountFrequency (%)
0.04028866073 1
 
< 0.1%
0.05990270666 1
 
< 0.1%
0.08357962633 1
 
< 0.1%
0.0877162392 1
 
< 0.1%
0.08798607948 1
 
< 0.1%
0.09889968918 1
 
< 0.1%
0.1179272727 1
 
< 0.1%
0.1220265838 1
 
< 0.1%
0.1236240723 2
0.1%
0.1238412933 4
0.1%
ValueCountFrequency (%)
255.9799786 1
< 0.1%
94.99953199 1
< 0.1%
57.51180184 1
< 0.1%
55.74624436 1
< 0.1%
42.77339066 1
< 0.1%
33.01404136 1
< 0.1%
32.75418639 1
< 0.1%
32.64893483 1
< 0.1%
31.59246692 1
< 0.1%
30.69258267 1
< 0.1%

customer
Real number (ℝ)

Distinct2990
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean229374.2
Minimum18
Maximum462568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2024-03-12T21:16:31.144056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile23700.05
Q1111568
median227825.5
Q3346049.75
95-th percentile441383.25
Maximum462568
Range462550
Interquartile range (IQR)234481.75

Descriptive statistics

Standard deviation134225.85
Coefficient of variation (CV)0.58518286
Kurtosis-1.2096756
Mean229374.2
Median Absolute Deviation (MAD)116798.5
Skewness0.029408129
Sum6.881226 × 108
Variance1.8016578 × 1010
MonotonicityNot monotonic
2024-03-12T21:16:31.182257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
291624 2
 
0.1%
161522 2
 
0.1%
408655 2
 
0.1%
288242 2
 
0.1%
382380 2
 
0.1%
445092 2
 
0.1%
68402 2
 
0.1%
266942 2
 
0.1%
404996 2
 
0.1%
458440 2
 
0.1%
Other values (2980) 2980
99.3%
ValueCountFrequency (%)
18 1
< 0.1%
53 1
< 0.1%
156 1
< 0.1%
219 1
< 0.1%
226 1
< 0.1%
324 1
< 0.1%
384 1
< 0.1%
657 1
< 0.1%
679 1
< 0.1%
959 1
< 0.1%
ValueCountFrequency (%)
462568 1
< 0.1%
462361 1
< 0.1%
462349 1
< 0.1%
462258 1
< 0.1%
462239 1
< 0.1%
462210 1
< 0.1%
461780 1
< 0.1%
461775 1
< 0.1%
461224 1
< 0.1%
461207 1
< 0.1%

merchant
Real number (ℝ)

Distinct2954
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean298357.96
Minimum106
Maximum591870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2024-03-12T21:16:31.221251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum106
5-th percentile29239.9
Q1151737.75
median301043
Q3446427.25
95-th percentile567709.45
Maximum591870
Range591764
Interquartile range (IQR)294689.5

Descriptive statistics

Standard deviation170566.9
Coefficient of variation (CV)0.57168543
Kurtosis-1.1866248
Mean298357.96
Median Absolute Deviation (MAD)147273.5
Skewness-0.015358107
Sum8.9507388 × 108
Variance2.9093067 × 1010
MonotonicityNot monotonic
2024-03-12T21:16:31.258467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
260618 3
 
0.1%
92994 3
 
0.1%
449964 3
 
0.1%
278688 3
 
0.1%
419424 3
 
0.1%
227282 2
 
0.1%
583458 2
 
0.1%
143284 2
 
0.1%
19752 2
 
0.1%
577648 2
 
0.1%
Other values (2944) 2975
99.2%
ValueCountFrequency (%)
106 1
< 0.1%
139 1
< 0.1%
228 1
< 0.1%
400 1
< 0.1%
573 1
< 0.1%
584 1
< 0.1%
734 1
< 0.1%
1022 1
< 0.1%
1026 1
< 0.1%
1311 1
< 0.1%
ValueCountFrequency (%)
591870 1
< 0.1%
591739 1
< 0.1%
591587 1
< 0.1%
591099 1
< 0.1%
590792 1
< 0.1%
590789 1
< 0.1%
590726 1
< 0.1%
590401 1
< 0.1%
590374 1
< 0.1%
590364 1
< 0.1%

Interactions

2024-03-12T21:16:28.046867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:11.142366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:11.941889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:12.664042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:13.581900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:14.357142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:15.083707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:15.856350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:16.806440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:17.610517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:18.372947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:19.121665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:20.155182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:20.914541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:21.674874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:22.443867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:23.227080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:24.016632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:25.037047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:25.829528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:26.599294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:27.325415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:28.082705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:11.181585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:11.977808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:12.700934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:13.619047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:14.391330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:15.120798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:15.893622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:16.847646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:17.646527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:18.410100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:19.157661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:20.192697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:20.950332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:21.711695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:22.483762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:23.266664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:24.052566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:25.075672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:25.867433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:26.634527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:27.362122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:28.114596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:11.217240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:12.008192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:12.732424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:13.651899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:14.422113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:15.153063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:15.925366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:16.882354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:17.678156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:18.441983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:19.190051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:20.227782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:20.983474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:21.743491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:22.518427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:23.300055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:24.084019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:25.109599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:25.899545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:26.665065image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:27.392620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:28.150702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:11.254420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:12.042233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:12.768800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:13.688061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:14.456223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:15.188749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:15.962154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:16.922481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:17.713851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:18.477523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:19.235685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:20.264258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:21.019061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:21.779253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:22.557936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:23.336724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:24.118342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:25.145784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:25.936402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:26.700272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:27.427221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:28.186707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:11.292170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:12.078097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:12.806463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:13.723430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:14.490805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:15.226381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:15.998796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:16.961254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:17.749832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:18.513491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:19.277183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:20.301785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:21.056490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:21.815920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:22.596860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:23.373531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:24.424015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:25.182695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:25.973185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:26.735061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:27.461769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:28.218330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:11.326975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:12.107823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:12.838218image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:13.757717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:14.520896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:15.258872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:16.032057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:16.997312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:17.781608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2024-03-12T21:16:21.400449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:22.166751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:22.949661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:23.723703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:24.758986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:25.541349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:26.319867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:27.059113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:27.781547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:28.560714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:11.688214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:12.431556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:13.182060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:14.108387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:14.849131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:15.603157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:16.556122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:17.360103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:18.127788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:18.881871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:19.679583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:20.676869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:21.434318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:22.200594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:22.982916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:23.759608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:24.794311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:25.576211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:26.353673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:27.092372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:27.815454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:28.598437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:11.727286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:12.469102image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:13.371031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:14.146358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:14.885216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:15.641277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:16.593634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:17.398292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:18.165291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:18.919233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:19.943030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:20.712748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:21.471812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:22.238825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:23.020205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:23.798687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:24.830742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:25.615520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:26.391666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:27.128821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:27.850468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:28.631704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:11.762379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:12.500924image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:13.406266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:14.181615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:14.917387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:15.676480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:16.627007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:17.432948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:18.199375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:18.952710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:19.978540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:20.745536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:21.504762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:22.271748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:23.053569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:23.833203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:24.864112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:25.649990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:26.424490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:27.160476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:27.882439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:28.667751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:11.801347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:12.537045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:13.444364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:14.219759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:14.954363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:15.715076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:16.664758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:17.471344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:18.236858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:18.988610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:20.018579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:20.781828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:21.541802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:22.308499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:23.089737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:23.872143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:24.901529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:25.687508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:26.462325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:27.197220image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:27.917435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:28.704270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:11.837585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:12.570635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:13.481578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:14.256222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:14.987745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:15.752023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:16.702791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:17.508435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:18.272919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:19.024526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:20.055071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:20.816981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:21.577968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:22.344563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:23.126381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:23.910055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:24.938347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:25.725324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:26.498045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:27.230955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:27.952380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:28.736496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:11.871747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:12.601328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:13.515192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:14.289532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:15.019658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:15.786246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:16.737626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:17.541433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:18.306006image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:19.057501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:20.089033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:20.847262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:21.610576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:22.375174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:23.159272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:23.945695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:24.972059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:25.759187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:26.531416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:27.260789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:27.983550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:28.767778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:11.903938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:12.631335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:13.547212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:14.322391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:15.050287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:15.819679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:16.771036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:17.575083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:18.338031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:19.088037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:20.120360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:20.879038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:21.640773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:22.407785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:23.193247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:23.979406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:25.002854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:25.793170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:26.563521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:27.292227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-12T21:16:28.013338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2024-03-12T21:16:31.300420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
customer_spend_01customer_spend_02customer_spend_03customer_spend_05customer_spend_06customer_spend_07merchant_spend_06merchant_profile_01customer_profile_01customer_profile_02distance_04merchant_profile_02merchant_spend_09merchant_profile_03customer_digital_activity_01merchant_spend_10customer_profile_03customer_digital_activity_02customer_profile_04distance_05customermerchantind_recommendedactivation
customer_spend_011.0000.2220.1450.4230.0950.089-0.0210.0340.2780.221-0.0060.0060.009-0.007-0.0160.0460.1710.0550.0770.020-0.0420.0030.0000.000
customer_spend_020.2221.0000.8400.7720.8250.812-0.0200.0560.6610.617-0.0380.0230.0350.0180.0040.0520.4760.2290.112-0.050-0.0610.0160.0440.071
customer_spend_030.1450.8401.0000.8560.9160.893-0.0090.0520.7310.697-0.0170.0120.0170.0040.0340.0410.5780.2660.049-0.049-0.0240.0160.0710.072
customer_spend_050.4230.7720.8561.0000.8410.827-0.0250.0710.7990.749-0.0120.0280.0170.0000.0320.0790.6080.2750.131-0.051-0.0650.0220.0140.000
customer_spend_060.0950.8250.9160.8411.0000.992-0.0100.0670.7470.718-0.0250.0160.0130.0110.0360.0690.6310.2940.108-0.065-0.0470.0330.0810.078
customer_spend_070.0890.8120.8930.8270.9921.000-0.0150.0760.7390.715-0.0240.0200.0100.0080.0360.0820.6300.2880.139-0.066-0.0630.0360.0800.077
merchant_spend_06-0.021-0.020-0.009-0.025-0.010-0.0151.000-0.063-0.019-0.029-0.097-0.2570.2030.101-0.024-0.355-0.012-0.039-0.0660.1080.0210.0250.0000.011
merchant_profile_010.0340.0560.0520.0710.0670.076-0.0631.0000.0400.043-0.0000.118-0.397-0.3980.0200.3610.056-0.0000.1540.003-0.088-0.0060.0210.024
customer_profile_010.2780.6610.7310.7990.7470.739-0.0190.0401.0000.831-0.0140.0420.0430.0200.0080.0640.5600.2890.108-0.075-0.0440.0440.0480.000
customer_profile_020.2210.6170.6970.7490.7180.715-0.0290.0430.8311.000-0.0210.0380.0360.0170.0360.0650.5500.2940.112-0.062-0.0440.0360.0000.000
distance_04-0.006-0.038-0.017-0.012-0.025-0.024-0.097-0.000-0.014-0.0211.0000.079-0.077-0.038-0.0190.147-0.002-0.003-0.0050.147-0.012-0.0230.0000.000
merchant_profile_020.0060.0230.0120.0280.0160.020-0.2570.1180.0420.0380.0791.000-0.198-0.5560.0300.3430.0430.0090.137-0.159-0.075-0.0170.0000.000
merchant_spend_090.0090.0350.0170.0170.0130.0100.203-0.3970.0430.036-0.077-0.1981.0000.736-0.067-0.4480.0220.0690.014-0.110-0.0010.0090.0280.057
merchant_profile_03-0.0070.0180.0040.0000.0110.0080.101-0.3980.0200.017-0.038-0.5560.7361.000-0.067-0.1570.0040.042-0.022-0.0320.0380.0220.0710.023
customer_digital_activity_01-0.0160.0040.0340.0320.0360.036-0.0240.0200.0080.036-0.0190.030-0.067-0.0671.0000.0360.029-0.046-0.0180.0990.014-0.0140.0010.051
merchant_spend_100.0460.0520.0410.0790.0690.082-0.3550.3610.0640.0650.1470.343-0.448-0.1570.0361.0000.077-0.0020.234-0.041-0.0910.0060.0480.000
customer_profile_030.1710.4760.5780.6080.6310.630-0.0120.0560.5600.550-0.0020.0430.0220.0040.0290.0771.0000.2670.116-0.087-0.0460.0260.0240.033
customer_digital_activity_020.0550.2290.2660.2750.2940.288-0.039-0.0000.2890.294-0.0030.0090.0690.042-0.046-0.0020.2671.0000.066-0.183-0.0100.0170.1110.000
customer_profile_040.0770.1120.0490.1310.1080.139-0.0660.1540.1080.112-0.0050.1370.014-0.022-0.0180.2340.1160.0661.000-0.159-0.5690.0180.0000.000
distance_050.020-0.050-0.049-0.051-0.065-0.0660.1080.003-0.075-0.0620.147-0.159-0.110-0.0320.099-0.041-0.087-0.183-0.1591.0000.056-0.0420.0000.000
customer-0.042-0.061-0.024-0.065-0.047-0.0630.021-0.088-0.044-0.044-0.012-0.075-0.0010.0380.014-0.091-0.046-0.010-0.5690.0561.000-0.0100.0210.073
merchant0.0030.0160.0160.0220.0330.0360.025-0.0060.0440.036-0.023-0.0170.0090.022-0.0140.0060.0260.0170.018-0.042-0.0101.0000.0490.000
ind_recommended0.0000.0440.0710.0140.0810.0800.0000.0210.0480.0000.0000.0000.0280.0710.0010.0480.0240.1110.0000.0000.0210.0491.0000.000
activation0.0000.0710.0720.0000.0780.0770.0110.0240.0000.0000.0000.0000.0570.0230.0510.0000.0330.0000.0000.0000.0730.0000.0001.000

Missing values

2024-03-12T21:16:28.824559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-12T21:16:28.936278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-12T21:16:29.045746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ind_recommendedactivationcustomer_spend_01customer_spend_02customer_spend_03customer_spend_05customer_spend_06customer_spend_07merchant_spend_06merchant_profile_01customer_profile_01customer_profile_02distance_04merchant_profile_02merchant_spend_09merchant_profile_03customer_digital_activity_01merchant_spend_10customer_profile_03customer_digital_activity_02customer_profile_04distance_05customermerchant
68447750028.5516672.010.05649.6551.044.049.0103.0240.761960.501.0000000.210526368.02394.01.00000065.44000020.6101761.33333343.019.821790234367116342
13110930087.5000002.05.01350.4821.021.033.0326.0330.83225.001.8928570.03703724999.036593.00.31111157.3571432.0885851.000000130.09.10732712997938976
53071720051.27887114.0114.018484.08698.0296.080.0406.02438.382767.046.5000000.33333313770.07236.01.000000104.94000099.8659107.00000091.012.508751247824322356
77504521029.93362816.0144.018451.651264.0330.0-999.0101.03375.87719.211.0000000.600000NaNNaN0.2592596.53000091.8087654.00000090.00.136353378972583909
97023910049.6776477.032.08772.82221.0158.023.0408.0815.45735.489.5000000.0400006190.010220.00.950000187.15500044.5478941.500000492.09.60090264420372741
211122000NaNNaNNaNNaNNaNNaN-999.0406.00.000.007.0000000.333333NaNNaN0.78125046.8000000.0000000.000000104.010.95797016222773194
54698450042.23571422.0116.014213.46624.0264.03.0314.014402.6813401.95NaN0.5811971432.0186.00.000000250.00000097.7445014.666667159.02.409571239182512739
58881230036.6365229.062.06139.74223.0145.024.0202.03404.275000.0035.5000000.59574519236.0861.00.8000003.85526347.23247732.500000123.07.674247201293436105
486560000NaNNaNNaNNaNNaNNaNNaNNaN0.000.008.3333330.09302320031.033068.00.52000067.0312500.0000000.00000030.07.153737353939429398
75451751036.0683336.016.01420.83125.0104.090.0415.0271.83400.00NaNNaNNaNNaN0.142857NaN66.88440514.333333322.02.31987775946493616
ind_recommendedactivationcustomer_spend_01customer_spend_02customer_spend_03customer_spend_05customer_spend_06customer_spend_07merchant_spend_06merchant_profile_01customer_profile_01customer_profile_02distance_04merchant_profile_02merchant_spend_09merchant_profile_03customer_digital_activity_01merchant_spend_10customer_profile_03customer_digital_activity_02customer_profile_04distance_05customermerchant
39634500085.99225812.0147.026242.46442.0237.017.0101.03937.296000.002.1005590.09106566231.088333.00.75000023.30000082.5872915.500000222.01.236361351023242435
83905960055.0072739.088.011001.95365.0193.028.0101.02154.242524.391.0000000.27536250250.022713.00.32432428.888333100.0000004.000000180.01.504913200555161978
7401352001.0000001.02.0534.1223.021.094.0101.0416.09167.2276.000000NaNNaNNaN0.54545533.2200006.0460223.666667147.04.10861122780158127
98551290034.6515007.061.06036.25195.0114.086.0103.0853.67100.0010.1428570.1304354734.04266.00.07142911.45000060.5183820.50000026.00.9777353746845391
93628440045.75111115.083.07408.15314.0183.048.0420.0841.011425.061.0000000.1800006651.05973.00.24000027.53500069.3655940.16666743.05.56072522565812409
11205910063.6755567.025.03240.79155.0115.018.0416.0179.66247.193.6743420.21084334562.019009.00.00000023.95000089.2698721.500000142.05.19179812867976945
62693440027.9172226.018.03351.4186.071.052.0326.01849.12145.7310.0000000.2500004580.016202.00.860000187.0000006.2082993.500000150.05.006359204678577648
92050010057.08495325.0340.062357.221474.0336.027.0307.022770.2012416.901.1666670.4347837324.03438.00.657143454.76000085.1098792.833333217.00.189227206993234195
16214980045.1615009.042.09836.26493.0252.024.0319.03646.26620.665.7500000.22727310889.010219.00.375000265.00000077.4232442.500000170.03.20430821294235647
12039710025.3371432.010.0327.2816.09.07.0101.00.000.002.5694010.07508013517.092250.00.13043555.7403571.0382561.16666785.01.350307343961168301